Early subspace face recognition methods such as LDA and Bayesian face reduce the intra-personal variations due to poses, illuminations, expressions, ages, and occlusions while the inter-personal variations are enlarged. For example, LDA approximates inter- and intra-personal face variations by using two linear subspaces and finds the projection directions to maximize the ratio between them.
More recent studies have also targeted the same goal, either explicitly or implicitly. For example, metric learning is proposed to map face images to some feature representation such that face images of the same identity are close to each other while those of different identities stay apart. However, these models are much limited by their linear nature or shallow structures, while inter- and intra-personal variations are complex, highly nonlinear, and observed in high-dimensional image space.
In recent years, a great deal of efforts has been made to learn effective features for face recognition with deep models using either the identification or verification supervisory signals. The learned features with identification signal have achieved accuracies of around 97.45% on LFW.
The idea of jointly solving the classification and verification tasks was applied to general object recognition, with the focus on improving classification accuracy on fixed object classes instead of hidden feature representations.